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Smart Devices (ICSD), International Conference on

作者： Pooja Malik Praveen Kumar Malik Gotte Ranjith Kumar Jyoti S. Bali Anshul Mahajan M. Muhsen Hassan School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India School of Computer Science & Artificial Intelligence SR University Warangal Dept of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune The Islamic University Najaf Iraq

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

The extensive use of technology across several Modern civilization is clearly impacted by technology, with cutting-edge techniques and tools playing critical roles in sectors including healthcare, business, agriculture, and education. Processes have been transformed by these improvements, which have increased their dependability and efficiency. With the incorporation of technology, agriculture has made enormous strides, with techniques like aquaponics developing as extremely effective and sustainable systems. Aquaponics is a novel farming method that combines hydroponics (soilless plant cultivation) and aquaculture (fish farming) to grow vegetables and fish at the same time. But the performance of this incorporated system relies upon on careful observation of a number of vital elements, such as water high-quality, pH tiers, temperature, and dissolved oxygen levels. To keep the health and growth of both fish and flora in an aquaponics device, it's miles critical to hold perfect situations. This highlights the significance of technological intervention, especially via the integration of Internet of Things (IoT) era, and calls for precision in regulating the input time table for fish feed and plant vitamins.

关键词： Technological innovation Schedules Urban areas Vegetation Water conservation Fish Safety Internet of Things Sustainable development Farming

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Predicting Satisfied User and Machine Ratio for Compressed Images: A Unified Approach

arXiv

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arXiv 2024年

作者： Zhang, Qi Wang, Shanshe Zhang, Xinfeng Ma, Siwei Pan, Jingshan Gao, Wen National Research Engineering Center of Visual Technology School of Computer Science Peking University Beijing China University of Chinese Academy of Sciences Beijing China National Supercomputer Center in Jinan Jinan China

Nowadays, high-quality images are pursued by both humans for better viewing experience and by machines for more accurate visual analysis. However, images are usually compressed before being consumed, decreasing their quality. It is meaningful to predict the perceptual quality of compressed images for both humans and machines, which guides the optimization for compression. In this paper, we propose a unified approach to address this. Specifically, we create a deep learning-based model to predict Satisfied User Ratio (SUR) and Satisfied Machine Ratio (SMR) of compressed images simultaneously. We first pre-train a feature extractor network on a large-scale SMR-annotated dataset with human perception-related quality labels generated by diverse image quality models, which simulates the acquisition of SUR labels. Then, we propose an MLP-Mixer-based network to predict SUR and SMR by leveraging and fusing the extracted multi-layer features. We introduce a Difference Feature Residual Learning (DFRL) module to learn more discriminative difference features. We further use a Multi-Head Attention Aggregation and Pooling (MHAAP) layer to aggregate difference features and reduce their redundancy. Experimental results indicate that the proposed model significantly outperforms state-of-the-art SUR and SMR prediction methods. Moreover, our joint learning scheme of human and machine perceptual quality prediction tasks is effective at improving the performance of both. © 2024, CC BY.

关键词： Image quality

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Restoration of Defective Cloud Images from Meteorological Satellites

Restoration of Defective Cloud Images from Meteorological Sa...

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2023 China Automation Congress, CAC 2023

作者： Xu, Ruiyao Zhou, Zhixuan Zhao, Xintong Wang, Congcong Liu, Ding School of Mechano-Electronic Engineering Xidian University Xi'an710071 China Hangzhou Institute of Technology Xidian University Intelligent Robot Laboratory Hangzhou311231 China Beijing Global Metroute Technology Co. Ltd Huafeng Weather Media Group Beijing10081 China Ministry of Education Engineering Research Center of Learning-Based Intelligent System School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798350303759

Meteorological satellites are inevitably disturbed in information transmission, which leads to missing bars on the cloud images and thus makes it impossible for meteorologists to give accurate weather predictions. The UNet-like networks have a hierarchical feature map to obtain rich multi-scale contextual features. In addition, they can enhance the image reconstruction process using jump connections between the encoder and decoder. Therefore, based on deep learning theory, this paper proposes a method to restore defective satellite images using UNet and Swin-U'Net, which are both in shape of 'U'. Moreover, the work selects the infrared satellite cloud image ir107 in the SEVIR dataset as the base dataset, from which the defective dataset is artificially generated. By comparing the restoration results of the two networks, the work concludes that the UNet network performs better than the Swin-UNet network in the image restoration function, which verifies the effectiveness of the network in the image restoration function. © 2023 IEEE.

关键词： Image reconstruction

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Hanging a T-Shirt: A Step Towards Deformable Peg-in-Hole Manipulation with Multimodal Tactile Feedback

Hanging a T-Shirt: A Step Towards Deformable Peg-in-Hole Man...

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IEEE International Conference on robotics and Biomimetics

作者： Yipai Du Shoaib Aslam Michael Yu Wang Bertram E. Shi The Hong Kong University of Science and Technology Hong Kong Hong Kong Center for Construction Robotics (InnoHK center supported by HONG KONG ITC) Hong Kong Department of Mechanical and Mechatronis Engineering University of Engineering and Technology Lahore (Faisalabad Campus) Pakistan School of Engineering Great Bay University Dongguan Guangdong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Peg-in-hole manipulation has been a long-standing problem in robotics due to its broad application in both domestic and industrial domains. Due to advances in perception and modeling, deformable object manipulation is attracting increasing attention. This paper focuses on the intersection of these problems, where a hole must be deformed to allow peg entry. A common domestic application of this task is to pass a clothes hanger through a T-shirt's neckline to hang it. We demonstrate that the complexity of the problem can be reduced by using multimodal tactile feedback from a GelSight sensor. High-resolution contact detection helps to localize grasp to suitable locations on the T-shirt. Using tactile feedback to track a force trajectory, our algorithm can manipulate T-shirts of varying size and stiffness so that their neckline encloses the hanger. Our experimental results show consistency with the theoretical analysis. We anticipate that our proposed method will be more broadly applicable to other problems requiring simultaneous estimation and execution of force trajectory for elastic objects.

关键词： Visualization Service robots Force Tactile sensors Estimation Robustness Trajectory Complexity theory Sensors Planning

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How Well Do CMIP6 and CMIP5 Models Simulate the Climatological Seasonal Variations in Ocean Salinity?

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Advances in Atmospheric sciences 2022年第10期39卷 1650-1672,I0005-I0015页

作者： Yuanxin LIU Lijing CHENG Yuying PAN Zhetao TAN John ABRAHAM Bin ZHANG Jiang ZHU Junqiang SONG College of Meteorology and Oceanography College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Center for Ocean Mega-Science Chinese Academy of SciencesQingdao 266071China University of Chinese Academy of Sciences Beijing 100049China School of Engineering University of St.Thomas2115 Summit Ave.St PaulMN 55105USA Marine Science Data Center Institute of OceanologyChinese Academy of SciencesQingdao 266071China

This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 *** general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m ***,this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean,with a global average of−0.44 g kg^(−1) for the sea surface salinity(SSS)and−0.26 g kg^(−1) for the 0-1000-m averaged salinity(S1000)compared with the CMIP5 multimodel mean(−0.25 g kg^(−1) for the SSS and−0.07 g kg^(−1) for the S1000).In terms of the seasonal variation,both CMIP6 and CMIP5 models show positive(negative)anomalies in the first(second)half of the year in the global average SSS and *** model-simulated variation in SSS is consistent with the observations,but not for S1000,suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface *** CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S-20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian *** assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.

关键词： salinity climatology seasonal cycle CMIP5 CMIP6 upper ocean

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A Transverse-Read-assisted Fast Valid-Bits Collection in Stochastic Computing MACs for Energy-Efficient in-RTM DNNs

arXiv

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arXiv 2024年

作者： Wang, Jihe Zhang, Zhiying Dong, Xingwu Wang, Danghui School of Computer Science Northwestern Polytechnical University Engineering Research Center of Embedded System Integration Ministry of Education Shaanxi Xi’an710072 China AVIC ACTRI China

It looks very attractive to coordinate racetrack-memory (RM) and stochastic-computing (SC) jointly to build an ultra-low power neuron-architecture. However, the above combination has always been questioned in a fatal weakness that the heavy valid-bits collection of RM-MTJ, a.k.a. accumulative parallel counters (APCs), cannot physically match the requirement for energy-efficient in-memory DNNs. Fortunately, a recently developed Transverse-Read (TR) provides a lightweight collection of valid-bits by detecting domain-wall resistance between a couple of MTJs on a single nanowire. In this work, we first propose a neuron-architecture that utilizes parallel TRs to build an ultra-fast valid-bits collection for SC, in which, a vector multiplication is successfully degraded as swift TRs. To solve huge storage for full stochastic sequences caused by the limited TR banks, a hybrid coding, pseudo-fractal compression, is designed to generate stochastic sequences by segments. To overcome the misalignment by the parallel early-termination, an asynchronous schedule of TR is further designed to regularize the vectorization, in which, the valid-bits from different lanes are merged in multiple RM-stacks for vector-level valid-bits collection. However, an inherent defect of TR, i.e., neighbor parts cannot be accessed simultaneously, could limit the throughput of the parallel vector multiplication, therefore, an interleaving data placement is used for full utilization of memory bus among different vectors. The results show that the SC-MAC assisted with TR achieves 2.88 × −4.40×speedup compared to CORUSCANT, at the same time, energy consumption is reduced by 1.26 × −1.42×. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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A Comparative Analysis of Time Series Transformers and Alternative Deep Learning Models for SSVEP Classification

A Comparative Analysis of Time Series Transformers and Alt...

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Workshop held at the 12th International Conference on Model and Data engineering, MEDI 2023

作者： Ali, Heba Elzemity, Adel Oghostinos, Amir E. Selim, Sahar Center for Informatics Science Nile University Sheikh Zayed City12588 Egypt School of Information Technology and Computer Science Sheikh Zayed City12588 Egypt School of Engineering Nile University Sheikh Zayed City12588 Egypt

ISBN: (纸本)9783031557286

Steady State Visually Evoked Potentials (SSVEPs) are intrinsic responses to specific visual stimulus frequencies. When the retina is activated by a frequency ranging from 3.5 to 75 Hz, the brain produces electrical activity at the same frequency as the visual signal, or its multiples. Identifying the preferred frequencies of neurocortical dynamic processes is a benefit of SSVEPs. However, the time consumed during calibration sessions limits the number of training trials and gives rise to visual fatigue since there is significant human variation across and within individuals over time, which weakens the effectiveness of the individual training data. To address this issue, we propose a novel cross-subject-based classification method to enhance the robustness of SSVEP classification by employing cross-subject similarity and variability. Through an efficient time-series transformer, we compared Time Series Transformers (TST) with different deep learning approaches in the literature. We utilized the TST to speed up calibration processes and improve classification precision for new users. Then we compare this technique to other techniques: EEGNet, FBtCNN, and C-CNN. Our suggested framework’s outcomes are validated using two datasets with two different time window lengths. The experimental results suggest that cross-subject time-series transformers and EEGNet achieve better performance with specific subjects than state-of-the-art techniques when compared to other techniques that have high potential for building high-speed BCIs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Brain computer Interface C-CNN EEGNet SSVEP Time Series Transformer (TST)

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans 7

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

引用

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Sarpong, Kwabena Butun, Ertan Talo, Muhammed Abualkebash, Humam Ukwuoma, Chiagoziem C. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Sejong University DaeyangAI Center Department of Artificial Intelligence Seoul Korea Republic of Firat University Department of Computer Engineering Elaziǧ Turkey Firat University Department of Software Engineering Elazig Turkey Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

ISBN: (纸本)9798350342154

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (ViT). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile ViT is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs. © 2023 IEEE.

关键词： COVID-19

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Online and Offline Hybrid Teaching Mode Based on Scenario Cases: A Case Study of MySQL Databases 4

Online and Offline Hybrid Teaching Mode Based on Scenario Ca...

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4th International Conference on computer science and Educational Informatization, CSEI 2022

作者： You, Jinguo Wang, Ziwen Zhao, Xiaoxia Kou, Weili Zhang, Yunwei Zhang, Ting Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Computer Center Kunming University of Science and Technology Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Kunming China School of Information Engineering Jiaozuo University Henan Jiaozuo China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Teaching mode design is a process to achieve the learning goal. Under the background of information technology, online and offline hybrid teaching mode has become the new form of education, and the closely related teaching mode design plays an essential role in the success of teaching implementation. Scenario case teaching can stimulate students' interest in learning and improve their ability to analyze and solve problems. Taking the MySQL database course as an example, this paper introduces the design and implementation process of online and offline hybrid teaching modes based on scenario cases. The practical results demonstrate that the hybrid teaching mode far outperforms the traditional teaching effect in terms of the aspects of students' basic knowledge of databases, practical operation level, and self-learning ability. Furthermore, this teaching mode has gained popularity in the studying of both our university and league universities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Design

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An Open-source Hardware/Software Architecture and Supporting Simulation Environment to Perform Human FPV Flight Demonstrations for Unmanned Aerial Vehicle Autonomy

arXiv

引用

arXiv 2024年

作者： Xiao, Haosong Krisshnakumar, Prajit Pothuri, Jagadeswara P.K.V. Soni, Puru Butcher, Eric Chowdhury, Souma Mechanical and Aerospace Engineering University at Buffalo BuffaloNY United States Mechanical and Aerospace Engineering University at Buffalo BuffaloNY United States Computer Science and Engineering. University at Buffalo BuffaloNY United States Mechanical and Aerospace Engineering Center for Embodied Autonomy and Robotics University at Buffalo United States

Small multi-rotor unmanned aerial vehicles (UAVs), mainly quadcopters, are nowadays ubiquitous in research on aerial autonomy, including serving as scaled-down models for much larger aircraft such as vertical-take-off-and-lift vehicles for urban air mobility. Among the various research use cases, first-person-view (FPV) RC flight experiments allow for collecting data on how human pilots fly such aircraft, which could then be used to compare, contrast, validate, or train (via imitative learning) autonomous flight agents. While this could be uniquely beneficial, especially for studying UAV operation in contextually complex and safety-critical environments such as in human-UAV shared spaces, the lack of inexpensive and open-source hardware/software platforms that offer this capability along with low-level (complete access) to the underlying control software and data remains limited. To address this gap and significantly reduce barriers to human-guided autonomy research with UAVs, this paper presents an open-source software architecture implemented with an inexpensive in-house built quadcopter platform based on the F450 Quadcopter Frame. This setup uses two cameras to provide a dual-view FPV and an open-source flight controller, Pixhawk. The underlying software architecture, developed using the Python-based Kivy library, allows logging telemetry, GPS, control inputs, and camera frame data in a synchronized manner on the ground station computer. Since costs (time) and weather constraints typically limit numbers of physical outdoor flight experiments, this paper also presents a unique AirSim/Unreal Engine based simulation environment and graphical user interface (GUI) – aka digital twin – that provides a Hardware In The Loop (HITL) setup via the Pixhawk flight controller. We demonstrate the usability and reliability of the overall framework through a set of diverse physical FPV flight experiments and corresponding flight tests in the digital twin. Copyright © 2024, The Au

关键词： Graphical user interfaces

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